Cross Modal Alignment

Cross-modal alignment focuses on integrating information from different data modalities (e.g., text, images, audio) to create unified representations and uncover correlations between them. Current research emphasizes efficient and robust alignment methods, often employing parameter-efficient fine-tuning, lightweight encoders (like OneEncoder), and novel loss functions to address challenges such as noisy data and modality imbalances. This work is significant for improving the performance of various applications, including visual question answering, image retrieval, and speech recognition, by enabling more accurate and comprehensive understanding of multimodal data.